Adds test cases from github/codeql#21582 demonstrating false negatives:
- Address stored in class attribute (`self.bind_addr`)
- `os.environ.get` with insecure default value
- `gevent.socket` (alternative socket module)
The fix may look a bit obscure, so here's what's going on.
When we see `from . import helper`, we create an `ImportExpr` with level
equal to 1 (corresponding to the number of dots). To resolve such
imports, we compute the name of the enclosing package, as part of
`ImportExpr.qualifiedTopName()`. For this form of import expression, it
is equivalent to `this.getEnclosingModule().getPackageName()`. But
`qualifiedTopName` requires that `valid_module_name` holds for its
result, and this was _not_ the case for namespace packages.
To fix this, we extend `valid_module_name` to include the module names
of _any_ folder, not just regular package (which are the ones where
there's a `__init__.py` in the folder). Note that this doesn't simply
include all folders -- only the ones that result in valid module names
in Python.
Adds `hasOverloadDecorator` as a predicate on functions. It looks for
decorators called `overload` or `something.overload` (usually
`typing.overload` or `t.overload`). These are then filtered out in the
predicates that (approximate) resolving methods according to the MRO.
As the test introduced in the previous commit shows, this removes the
spurious resolutions we had before.
The ones that no longer require points-to no longer import
`LegacyPointsTo`. The ones that do use the specific
`...MetricsWithPointsTo` classes that are applicable.
With `ModuleVariableNode`s now appearing for _all_ global variables (not
just the ones that actually seem to be used), some of the tests changed
a bit. Mostly this was in the form of new flow (because of new nodes
that popped into existence). For some inline expectation tests, I opted
to instead exclude these results, as there was no suitable location to
annotate. For the normal tests, I just accepted the output (after having
vetted it carefully, of course).
This pull request introduces a new CodeQL query for detecting prompt injection vulnerabilities in Python code targeting AI prompting APIs such as agents and openai. The changes includes a new experimental query, new taint flow and type models, a customizable dataflow configuration, documentation, and comprehensive test coverage.
